The current study aims to evaluate the association between neck circumference (NC) and several cardio-metabolic risk factors, to compare it with well-established anthropometric indices, and to determine the cut-off point value of NC for predicting children at increased risk of metabolic syndrome, insulin resistance and low-grade systemic inflammation.
Gomez-Arbelaez et al BMC Pediatrics (2016) 16:31 DOI 10.1186/s12887-016-0566-1 RESEARCH ARTICLE Open Access Neck circumference as a predictor of metabolic syndrome, insulin resistance and low-grade systemic inflammation in children: the ACFIES study Diego Gomez-Arbelaez1,2,3 , Paul Anthony Camacho1, Daniel Dylan Cohen1,2, Sandra Saavedra-Cortes2, Cristina Lopez-Lopez4 and Patricio Lopez-Jaramillo1,2* Abstract Background: The current study aims to evaluate the association between neck circumference (NC) and several cardio-metabolic risk factors, to compare it with well-established anthropometric indices, and to determine the cut-off point value of NC for predicting children at increased risk of metabolic syndrome, insulin resistance and low-grade systemic inflammation Methods: A total of 669 school children, aged 8–14, were recruited Demographic, clinical, anthropometric and biochemical data from all patients were collected Correlations between cardio-metabolic risk factors and NC and other anthropometric variables were evaluated using the Spearman’s correlation coefficient Multiple linear regression analysis was applied to further examine these associations We then determined by receiver operating characteristic (ROC) analyses the optimal cut-off for NC for identifying children with elevated cardio-metabolic risk Results: NC was positively associated with fasting plasma glucose and triglycerides (p = 0.001 for all), and systolic and diastolic blood pressure, C-reactive protein, insulin and HOMA-IR (p < 0.001 for all), and negatively with HDL-C (p = 0.001) Whereas, other anthropometric indices were associated with fewer risk factors Conclusions: NC could be used as clinically relevant and easy to implement indicator of cardio-metabolic risk in children Keywords: Childhood obesity, Anthropometric measurements, Neck circumference, Metabolic syndrome, Low-grade systemic inflammation, Insulin resistance, Cardiometabolic risk, Latin America, Colombia Background The prevalence of obesity in children and adolescents is increasing worldwide and it is now recognized as an international public health concern [1] Epidemiological and clinical investigations have revealed that the association between obesity and cardiovascular and metabolic risk factors begins early in life [2, 3] Childhood obesity is associated with increased prevalence of hypertension, dyslipidemia, and abnormal glucose tolerance [2–4] Thus, identifying and controlling childhood obesity is an * Correspondence: jplopezj@gmail.com Dirección de Investigaciones, Fundación Oftalmológica de Santander FOSCAL, Floridablanca, Colombia Instituto MASIRA, Facultad de la Ciencias de la Salud, Universidad de Santander - UDES, Bucaramanga, Colombia Full list of author information is available at the end of the article important goal in the prevention of cardiovascular diseases (CVD) in later life [5] Although obesity is at the core of the development of CVD, appropriate anthropometric measures and cut-off points to identify children with elevated cardio-metabolic risk factors are not well established The most widely used method to categorize overweight and obese children and to predict cardiovascular and metabolic risk is the body mass index (BMI) [6] However, BMI has been considered as an imperfect measure of adiposity, because it does not distinguish between muscle mass and fat mass, and requires calculations and the use of charts that may not always be available [7, 8] Alternative measures to BMI such as waist-to-hip ratio (WHR) and waist circumference, which also give some © 2016 Gomez-Arbelaez et al Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated Gomez-Arbelaez et al BMC Pediatrics (2016) 16:31 indication of fat distribution, have been used as alternatives, but none of these have been accepted as a gold standard measure to identify cardiovascular and metabolic risk [9, 10] Both have limitations in distinguishing the contribution from ectopic adipose tissue and subcutaneous adipose tissue [11], which show strong and modest correlations to cardio-metabolic risk, respectively [12, 13] Prior studies have suggested that upper body fat plays a role in cardio-metabolic risk [14, 15], and neck circumference (NC) was proposed as a new measurement to evaluate overweight and obesity in children [16–18] NC has demonstrated to be an independent predictor of metabolic risk beyond BMI and waist circumference [15] and to be positively associated with insulin resistance and visceral adipose tissue in adults [19], but few studies have been conducted to determine its association with cardio-metabolic risk factors in children [20, 21] Hence, the aims of the present study were to evaluate the association between NC and several cardio-metabolic risk factors and to compare these associations with those of BMI and other wellestablished anthropometric indexes in a Latin American pediatric population Methods Study population During the 2011–2012 school year, we conducted the cross-sectional component of the ACFIES study (Association between Cardiorespiratory Fitness, Muscular Strength and Body Composition with Metabolic Risk Factors in Colombian Children) to identify the prevalence and associations of cardiovascular risk factors, in a sample of schoolchildren from both sexes, enrolled in public elementary and high schools (grades and 6), from the city of Bucaramanga, Colombia All the recruited participants met the general ACFIES inclusion criteria: age range to 14 years, not having any physical disability and be free of any acute infection lasting less than weeks before the inclusion Moreover, children were excluded if were using medications that could alter blood pressure, insulin resistance, glycemic levels and/or lipid profile The study protocol was in accordance with the Declaration of Helsinki and was approved by the Health Research Ethics Board of the Ophthalmological Foundation of Santander (FOSCAL) The children expressed their interest in participating in the study, and parents or legal guardians gave written informed consent, before the children were included in the study Anthropometric measurements and physical examination All physical assessments and anthropometric measurements were performed after an overnight fast (8 to 10 h), in duplicate by well-trained health workers For the analysis Page of we used the mean of the two measurements Participant’s body weight was measured to the nearest 0.1 kg on an electronic device (Tanita BC544, Tokyo, Japan), in underwear and without shoes, and height was measured to the nearest 0.1 cm using a mechanical stadiometer with platform (Seca 274, Hamburg, Germany), while participants were asked to stand erect with their head positioned in the Frankfort horizontal plane BMI was calculated by dividing body weight by the square of height (BMI = weight (kg)/ height (m)2) The weight status was classified according to Barlow et al [22] Neck circumference was measured to the nearest 0.1 cm using a tape measure The superior border of the tape measure was placed just below the laryngeal prominence and applied perpendicular to long axis of the neck Waist circumference was determined at the middle point between the lower edge of the ribs and the iliac anterior spine The measurement was made at the end of a normal expiration while the subject stood upright Hip circumference was measured over non-restrictive underwear at the level of the maximum extension of the buttocks posteriorly in a horizontal plane All circumferences were measured using a measuring tape with spring scale (Ohaus 8004-MA, NJ, USA) WHR was calculated as waist circumference divided by hip circumference Waist-to-height ratio (WHtR) was calculated by dividing waist circumference by height in cm The measurements were realized according to the procedures previously described by Lohman et al [23] Skinfold thickness was measured to the nearest 0.2 mm on the right side of the body at the triceps and subscapular sites using a skinfold caliper (Harpenden C-136, United Kingdom) and body fat percentage (%BF-Skinfold) estimated using skinfold equations described by Slaughter et al [24] Body fat percentage was also assessed by bioelectrical impedance analysis (BIA) (%BF-BIA) (Tanita BC544, Tokyo, Japan) Systolic blood pressure and diastolic blood pressure were determined after a resting period of 10 in the sitting position using an automatic and calibrated sphygmomanometer with a pediatric cuff (Omron HEM 757 CAN, Hoofddorp, Netherlands) Pubertal development was assessed by Tanner stage of breast development in girls and testicular volume in boys [25] Biochemical parameters Venous blood samples were collected in the morning at the same time (07:00 am to 09:00 am), after an overnight fast (8 to 10 h), and from the antecubital vein Participants were asked not to any prolonged exercise during the 24 h prior to the exam Blood samples were analyzed for concentrations of fasting plasma glucose and lipid profile (total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-C)) using a routine colorimetric method (Biosystems BTS-303 Photometric, Barcelona, Gomez-Arbelaez et al BMC Pediatrics (2016) 16:31 Page of Spain) High-sensitivity C-reactive protein (hs-CRP) was quantified using a turbid metric test (SPINREACT, Spain), and insulin levels were determined using an insulin microplate ELISA test (Monobind, USA) Samples were processed and analyzed in the clinical laboratory of bacteriology school of the University of Santander - UDES Homeostasis model assessment for insulin resistance (HOMA-IR) was calculated using the equation: HOMAIR = Fasting insulin (lU/ml) x Fasting glucose (mg/dl)/ 405 [26] inflammation according to gender, analyzes were made using the ROC (receiver operating characteristic) curves The statistical significance of each analysis was verified by the area under the ROC curve (AUCs) and by 95 % confidence intervals (95 % CI´s) The maximum values of the Youden’s index [29] were used as a criterion for selecting the optimum cut-off points All statistical analyzes were carried out using Stata statistical software, release 11.0 (Stata Corporation, College Station, TX, USA) A p < 0.05 was considered statistically significant Cardiovascular and metabolic risk definition Results For this study, the cardiovascular and metabolic risk in children and adolescents was defined according to a modified version of the National Health and Nutrition Examination Survey (NHANES) definition of metabolic syndrome (MetS) [27] The considered parameters were: increased waist circumference (≥75th percentile for age and sex of study cohort), elevated triglycerides (≥110 mg/dl), low HDL-C (≤40 mg/dl), elevated systolic blood pressure and/ or diastolic blood pressure (≥90 percentile for age, sex and height), and elevated fasting plasma glucose (≥100 mg/dl) MetS was defined by the presence of or more of the above criteria [27] Although the NHANES definition was not intended to be applied to children below 12 years of age, for the purposes of this study to enable comparisons to be made and as cardiovascular and metabolic alterations can be present in children from their earliest years of life [2, 3], we have defined the individual risk components of MetS across the complete sample of children aged between to 14 years Moreover, a value of ≥2.6 in HOMA-IR was considered to indicate insulin resistance [28], and values of hs-CRP ≥0.55 mg/dl (75th percentile in our study sample) were considered as low-grade systemic inflammation Descriptive statistics Statistical analysis Descriptive statistics were computed for variables of interest, and included mean values and standard deviations of continuous variables and absolute and relative frequencies of categorical factors Normality of distribution was checked for continuous variables using the Shapiro-Wilk test and by graphical methods Student’s t-test and Mann-Whitney test were used to assess potential differences in continuous variables We tested for differences in categorical variables using the Pearson’s chi-squared test (Chi2) Correlations between cardio-metabolic risk factors and anthropometric variables were evaluated using the Pearson’s correlation or Spearman’s correlation coefficient, according to normality of distributions Multiple linear regression analysis was applied to further examine these associations For selection of the cut-off points of NC that could identify MetS, insulin resistance and low-grade systemic As it has been previously reported [30, 31], a total of 669 children and adolescents were recruited during the crosssectional component of the ACFIES study, of which 351 (52.5 %) were boys The overall mean age was 11.5 ± 1.1 years Demographic, anthropometric and metabolic characteristics of the study population by sex are presented in Table Compared to the girls, mean systolic blood pressure, waist circumference, WHR, WHtR, NC and %BFSkinfold were significantly higher, while height, %BF-BIA, triglycerides, insulin and HOMA-IR were significantly lower in boys Among our study population, 85 (12.9 %) were overweight and 65 (9.8 %) were obese There were no statistically significant differences in weight status and BMI between both genders Sex-specific prevalences of MetS and its individual abnormalities, insulin resistance and lowgrade systemic inflammation were also estimated (Fig 1), and statistical differences were not found Correlation between anthropometric indexes and cardio-metabolic risk factors Correlations of anthropometric indexes and cardiometabolic risk factors are presented in Table for the total sample and by gender Z-score BMI was positively correlated with triglycerides, systolic and diastolic blood pressure, hs-CRP, insulin and HOMA-IR in both genders, and inversely correlated with HDL-C only in boys Z-score WC was positively correlated with triglycerides, systolic and diastolic blood pressure, insulin and HOMA-IR in both genders, with fasting plasma glucose and hs-CRP only in girls, and inversely correlated with HDL-C only in boys WHR was positively correlated only with triglycerides in both genders, with diastolic blood pressure, insulin and HOMAIR only in boys, and with hs-CRP only in girls WHtR was positively correlated with triglycerides, systolic and diastolic blood pressure, insulin and HOMA-IR in both genders, and with hs-CRP only in girls %BF-BIA was positively correlated with triglycerides, systolic and diastolic blood pressure, insulin and HOMA-IR in both genders, with hsCRP only in girls, and inversely correlated with HDL-C only in girls %BF-Skinfold was positively correlated with systolic and diastolic blood pressure, hs-CRP, insulin and Gomez-Arbelaez et al BMC Pediatrics (2016) 16:31 Page of Table Demographic, anthropometric and metabolic data Total (n = 669) Girls (n = 318) Boys (n = 351) 11.52 ± 1.13 11.52 ± 1.10 11.51 ± 1.16 114.51 ± 11.59 113.29 ± 11.72 115.58 ± 11.38b 73.78 ± 9.47 73.66 ± 8.97 73.86 ± 9.93 Weight (kg) 40.08 ± 10.07 40.33 ± 9.77 39.86 ± 10.35 Height (m) 1.45 ± 0.09 1.45 ± 0.08 1.44 ± 0.09b BMI (kg/m ) 18.87 ± 3.61 18.81 ± 3.52 18.93 ± 3.68 WC (cm) 65.95 ± 9.73 64.86 ± 9.02 66.92 ± 10.24b WHR 0.84 ± 0.08 0.81 ± 0.06 0.86 ± 0.09b WHtR 0.45 ± 0.06 0.44 ± 0.06 0.46 ± 0.06b NC (cm) 29.93 ± 2.39 28.40 ± 2.06 29.41 ± 2.55b %BF-BIA 20.47 ± 7.50 22.71 ± 6.89 18.43 ± 7.46b %BF-Skinfold 25.47 ± 11.37 24.63 ± 9.10 26.23 ± 13.04b Z-score BMI (kg/m2) -0.0004 ± 0.98 -0.0008 ± 0.98 -5.45-7 ± 0.98 Age (years)a a SBP (mmHg) a DBP (mmHg) a Anthropometric measures -0.038 ± 0.99 4.88 ± 0.99 -0.073 ± 0.99b 88.52 ± 12.56 87.87 ± 12.32 89.12 ± 12.76 TC (mg/dl) 159.23 ± 39.28 158.25 ± 39.04 160.13 ± 39.53 HDL-C (mg/dl) 75.34 ± 19.96 74.57 ± 19.82 76.04 ± 20.08 TG (mg/dl) 91.76 ± 52.37 94.07 ± 46.79 89.67 ± 56.97b hs-CRP (mg/dl) 0.89 ± 1.62 0.88 ± 1.52 0.89 ± 1.71 Insulin (lU/ml) 2.58 ± 2.61 2.91 ± 2.91 2.29 ± 2.26b HOMA-IR 0.57 ± 0.58 0.64 ± 0.66 0.50 ± 0.50b 29 (4.4) (2.9) 20 (5.8) Z-score WC (cm) -8 Biochemical measurementsa FPG (mg/dl) Weight status (n - %)d o Underweight o Normal weight 479 (72.8) 240 (77.2) 239 (68.9) o Overweight 85 (12.9) 42 (13.5) 43 (12.4) o Obese 65 (9.8) 20 (6.4) 45 (12.9) o1 368 (56.3) 149 (47.8) 219 (64.0)c o2 208 (31.8) 110 (35.3) 98 (28.7) o3 78 (11.9) 53 (16.9) 25 (7.3) Tanner stage (n - %)e SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, WC waist circumference, WHR waist-to-hip ratio, WHtR waist-to-height ratio, NC neck circumference, %BF-BIA body fat percentage – bioelectrical impedance analysis, %BF-Skinfold body fat percentage – skinfolds, FPG fasting plasma glucose, TC total cholesterol, HDL-C high-density lipoprotein cholesterol, TG triglycerides, hs-CRP high sensitivity C-reactive protein a Data are presented as mean ± standard deviation for continuous variables bMann-Whitney test p < 0.05 cPearson’s chi-squared test (Chi2) p